

'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    (DPMSolverSDEScheduler,)
				lowerCamelCase_		       =    10







				def  lowerCamelCase_	(       self     :		List[Any]		,					**UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		={
										    '''num_train_timesteps''': 1100,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''noise_sampler_seed''': 0,
										}

										config.update(**UpperCAmelCase__   )
										return config







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]		,					[0.00_02, 0.0_02, 0.02]   ):
																self.check_over_configs(beta_start=UpperCAmelCase__		,					beta_end=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.scheduler_classes[0]
										lowercase :							Dict    		=self.get_scheduler_config()
										lowercase :							Tuple    		=scheduler_class(**UpperCAmelCase__   )

										scheduler.set_timesteps(self.num_inference_steps   )

										lowercase :							int    		=self.dummy_model()
										lowercase :							Dict    		=self.dummy_sample_deter * scheduler.init_noise_sigma
										lowercase :							List[str]    		=sample.to(UpperCAmelCase__   )

										for i, t in enumerate(scheduler.timesteps   ):
																lowercase :							List[Any]    		=scheduler.scale_model_input(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							List[str]    		=model(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							str    		=scheduler.step(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
																lowercase :							Tuple    		=output.prev_sample

										lowercase :							int    		=torch.sum(torch.abs(UpperCAmelCase__   )   )
										lowercase :							Optional[Any]    		=torch.mean(torch.abs(UpperCAmelCase__   )   )

										if torch_device in ["mps"]:
																assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75   ) < 1E-2
																assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77   ) < 1E-3
										elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06   ) < 1E-2
																assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52   ) < 1E-3
										else:
																assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1E-2
																assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1E-3







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.scheduler_classes[0]
										lowercase :							str    		=self.get_scheduler_config(prediction_type='''v_prediction'''   )
										lowercase :							Tuple    		=scheduler_class(**UpperCAmelCase__   )

										scheduler.set_timesteps(self.num_inference_steps   )

										lowercase :							Optional[int]    		=self.dummy_model()
										lowercase :							str    		=self.dummy_sample_deter * scheduler.init_noise_sigma
										lowercase :							str    		=sample.to(UpperCAmelCase__   )

										for i, t in enumerate(scheduler.timesteps   ):
																lowercase :							Optional[int]    		=scheduler.scale_model_input(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							Any    		=model(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							List[str]    		=scheduler.step(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
																lowercase :							int    		=output.prev_sample

										lowercase :							Any    		=torch.sum(torch.abs(UpperCAmelCase__   )   )
										lowercase :							List[str]    		=torch.mean(torch.abs(UpperCAmelCase__   )   )

										if torch_device in ["mps"]:
																assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53   ) < 1E-2
																assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84   ) < 1E-3
										elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03   ) < 1E-2
																assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97   ) < 1E-3
										else:
																assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25   ) < 1E-2
																assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21   ) < 1E-3







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.scheduler_classes[0]
										lowercase :							List[str]    		=self.get_scheduler_config()
										lowercase :							List[Any]    		=scheduler_class(**UpperCAmelCase__   )

										scheduler.set_timesteps(self.num_inference_steps		,					device=UpperCAmelCase__   )

										lowercase :							List[Any]    		=self.dummy_model()
										lowercase :							Union[str, Any]    		=self.dummy_sample_deter.to(UpperCAmelCase__   ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																lowercase :							List[Any]    		=scheduler.scale_model_input(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							Optional[Any]    		=model(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							Tuple    		=scheduler.step(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
																lowercase :							List[str]    		=output.prev_sample

										lowercase :							Any    		=torch.sum(torch.abs(UpperCAmelCase__   )   )
										lowercase :							Tuple    		=torch.mean(torch.abs(UpperCAmelCase__   )   )

										if torch_device in ["mps"]:
																assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38   ) < 1E-2
																assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35   ) < 1E-3
										elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12   ) < 1E-2
																assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71   ) < 1E-3
										else:
																assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1E-2
																assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1E-3







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.scheduler_classes[0]
										lowercase :							Optional[Any]    		=self.get_scheduler_config()
										lowercase :							Union[str, Any]    		=scheduler_class(**UpperCAmelCase__		,					use_karras_sigmas=UpperCAmelCase__   )

										scheduler.set_timesteps(self.num_inference_steps		,					device=UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=self.dummy_model()
										lowercase :							str    		=self.dummy_sample_deter.to(UpperCAmelCase__   ) * scheduler.init_noise_sigma
										lowercase :							List[str]    		=sample.to(UpperCAmelCase__   )

										for t in scheduler.timesteps:
																lowercase :							int    		=scheduler.scale_model_input(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							int    		=model(UpperCAmelCase__		,					UpperCAmelCase__   )

																lowercase :							Dict    		=scheduler.step(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
																lowercase :							Optional[int]    		=output.prev_sample

										lowercase :							Any    		=torch.sum(torch.abs(UpperCAmelCase__   )   )
										lowercase :							List[Any]    		=torch.mean(torch.abs(UpperCAmelCase__   )   )

										if torch_device in ["mps"]:
																assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88   ) < 1E-2
																assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1E-2
										elif torch_device in ["cuda"]:
																assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25   ) < 1E-2
																assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1E-2
										else:
																assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72   ) < 1E-2
																assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1E-2













"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records